Various fairness metrics of interest have been proposed for classification [1]}, [2]}, [3]}, [4]}, ranking [5]}, [6]}, unsupervised learning [7]}, [8]}, [9]}, and online learning [10]}, [11]}, [12]}, [13]}.
These notions of fairness are usually categorized as either group fairness or individual fairness. In the case of group fairness, training a model to satisfy such fairness metrics and evaluating the metrics on a pre-trained model typically requires access to both the sensitive attribute and the label information on the same dataset.
Only recently, ML research
has explored the implications of using proxy attributes and the sensitivity of fair machine learning algorithms to this noisy demographic data [14]}, [15]}, [16]}, [17]}, [18]}.
